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Research Article Open access CC BY 4.0

Dry Leaf Biomass Stability of Stevia (Stevia rebaudiana) Clones over Different Environments

Niketa Yadav, Satbeer Singh, Ramesh Chauhan, Ashok Kumar, Probir Kumar Pal, Sanatsujat Singh

International Journal of Plant & Soil Science · pp. 80–89 · Published 16 Jul 2024

10.9734/ijpss/2024/v36i84837

Abstract

In order to identify stable high-yielding stevia (Stevia rebaudiana) genotypes, a multi-environment testing was conducted over four different growing environments. The experiments were conducted in a randomized block design, with three replications during 2019 and 2020. The combined analysis of variance showed significant variation for genotype, environment, and G×E interaction for all studied traits. The highest mean performance for all the traits revealed that Hoshiarpur has favorable conditions for stevia cultivation, and CSIR-IHBT-ST-1801 followed by CSIR-IHBT-ST-G12 were the best performers over all the locations. The Eberhart and Russell model-based stability parameters demonstrated that CSIR-IHBT-ST-1801 was a stable performer for dry leaf biomass, and that was also further confirmed by GGE biplot analysis. Primary shoots were major contributors to the dry leaf biomass, as indicated by the substantial positive leaf biomass contribution shown by Pearson's correlation coefficients. As a result, primary shoots might be utilized as selection criteria to increase the dry leaf biomass. The CSIR-IHBT-ST-1801 could be used as a stable high-yielding variety for the targeted regions and also, can be used for further stevia breeding programs.

Stevia multi-environment dry leaf biomass stability G×E interaction

References (31)

  1. 1 Orellana-Paucar AM. Steviol Glycosides from Stevia rebaudiana: An updated overview of their sweetening activity, pharmacological properties, and safety aspects. Molecules. 2023;28(3):1–12. DOI: 10.3390/molecules28031258 [DOI]
  2. 2 Chowdhury AI, Rahanur Alam M, Raihan MM, Rahman T, Islam S, Halima O. Effect of stevia leaves (Stevia rebaudiana Bertoni) on diabetes: A systematic review and meta-analysis of preclinical studies. Food Sci Nutr. 2022;10(3):2868–2878. DOI: 10.1002/fsn3.2904 [DOI]
  3. 3 Clemente C, Angelini LG, Ascrizzi R, Tavarini S. Stevia rebaudiana (Bertoni) as a multifunctional and sustainable crop for the mediterranean climate. MDPI, Agric. 2021;11(2):1–18. DOI: 10.3390/agriculture11020123 [DOI]
  4. 4 Wölwer-Rieck U. The leaves of Stevia rebaudiana (Bertoni), their constituents and the analyses thereof: A review. J Agric Food Chem. 2012; 60(4):886–895. DOI: 10.1021/jf2044907 [DOI]
  5. 5 Raspe DT, da Silva C, Cláudio da Costa S. Compounds from Stevia rebaudiana Bertoni leaves: An overview of non-conventional extraction methods and challenges. ELSEVIER -Food Biosci. 2022;46(101593):1–14. doi:10.1016/j.fbio.2022.101593 [DOI]
  6. 6 Yadav AK, Singh S, Dhyani D, Ahuja PS. A review on the improvement of stevia [Stevia rebaudiana (Bertoni)]. Can J Plant Sci. 2011;91(1):1–27. DOI: 10.4141/CJPS10086 [DOI]
  7. 7 Aranda-González I, Moguel-Ordoñez Y, Betancur-Ancona D. Validation of HPLC-UV method for determination of minor glycosides contained in Stevia rebaudiana Bertoni leaves. Biomed Chromatogr. 2015; 29(5):733–738. DOI: 10.1002/bmc.3349 [DOI]
  8. 8 Iatridis N, Kougioumtzi A, Vlataki K, Papadaki S, Magklara A. anti-cancer properties of Stevia rebaudiana; More than a Sweetener. MDPI-Molecules. 2022;27 (1362):1–20. DOI: 10.3390/molecules27041362 [DOI]
  9. 9 Salehi B, López MD, Martínez-López S, Victoriano M, Sharifi-Rad J, Martorell M. Stevia rebaudiana Bertoni bioactive effects: From in vivo to clinical trials towards future therapeutic approaches. Phyther Res. 2019;33(11):2904–2917. DOI: 10.1002/ptr.6478 [DOI]
  10. 10 Amien S, Maulana H, Ruswandi D, Nurjanah S. Stevia (Stevia rebaudiana B.) genotypes assessment for leaf yield stability through genotype by environment interactions, AMMI, and GGE biplot analysis. Sabrao J Breed Genet. 2022;54 (4):767–779. DOI: 10.54910/sabrao2022.54.4.8 [DOI]
  11. 11 Zhang J, Bell LN. Stability of the Stevia-derived sweetener rebaudioside a in solution as affected by ultraviolet light exposure. J Food Sci. 2017;82(4):897–903.DOI: 10.1111/1750-3841.13667 [DOI]
  12. 12 Momtazi-Borojeni AA, Esmaeili S-A, Abdollahi E, Sahebkar A. A review on the pharmacology and toxicology of steviol glycosides extracted from Stevia rebaudiana. Curr Pharm Des. 2016; 23(11):1616–1622. DOI:10.2174/1381612822666161021142835 [DOI]
  13. 13 RStudio. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA; 2022. Available:http://www.rstudio.com/.
  14. 14 Eberhart SA, Russell WA. Stability parameters for comparing varieties. Crop Sci. 1966;6(1):36–40. DOI:10.2135/cropsci1966.0011183x000600010011x [DOI]
  15. 15 Sheoran OP, Tonk DS, Kaushik LS, Hasija RC, Pannu RS. Statistical software package for agricultural research workers. recent adv inf theory, stat comput appl by DS Hooda RC Hasija Dep Mathmatics Stat CCS HAU Hisar. 1968;8(12):139–143. Available:http//14139232166/opstat/.
  16. 16 Singh M, Nara U, Kaur K, Pandey V, Pathak D, Sangha MK. Exploring GGE biplot, stability index, correlation, and clustering analysis for growth and seed yield-related traits in celery (Apium graveolens L.). J Appl Res Med Aromat Plants. 2024;40:100546. DOI:10.1016/j.jarmap.2024.100546 [DOI]
  17. 17 Kumar M, Patel M, Chauhan R, Tank C, Solanki S, Patel P Elucidation of genotype–environment interactions and genetic stability parameters for yield, quality and agromorphological traits in ashwagandha (Withania somnifera (L.) Dunal). J Genet. 2020;99(1):1–16. [DOI]
  18. 18 DOI:10.1007/s12041-020-01207-9 [DOI]
  19. 19 Shakya P, Thakur R, Sharan H, Yadav N, Kumar M, Chauhan R. GGE biplot and regression based multi-environment investigations for higher yield and essential oil content in German chamomile (Matricaria chamomilla L.). Ind Crops Prod 2023;193(December2022):116145. DOI:10.1016/j.indcrop.2022.116145 [DOI]
  20. 20 Pratibha, Yadav A, Rahevar P, Patil G, Patel K, Kumar S. Assessment of G × E interaction and stability parameters for quality, root yield and its associating traits in ashwagandha [Withania somnifera (L.) Dunal] germplasm lines. Ind Crops Prod 2024;208(117792):1–13. [DOI]
  21. 21 DOI: 10.1016/j.indcrop.2023.117792 [DOI]
  22. 22 Mustamu YA, Tjintokohadi K, Grüneberg WJ, Karuniawan A, Ruswandi D. Selection of superior genotype of sweet-potato in Indonesia based on stability and adaptability. Chil J Agric Res. 2018; 78(4):461–469. DOI:10.4067/S0718-58392018000400461 [DOI]
  23. 23 Enyew M, Feyissa T, Geleta M, Tesfaye K, Hammenhag C, Carlsson AS. Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench). PLoS One. 2021;16 (10):1–22. doi:10.1371/journal.pone.0258211 [DOI]
  24. 24 Krishnamurthy M, Wood F. Promoting Effective Searches and Use of Information by DNP Students: Getting the Write Start. Nurse Educ. 2018;43(2):63–64. DOI:10.1097/NNE.0000000000000457 [DOI]
  25. 25 Gupta A, Kumari M, Sharan H, Kumar A, Vikrant, Chauhan R. Multi-environment investigations using GGE biplot and regression model for higher spike yield and essential oil content in lavender (Lavandula angustifolia). J Essent Oil-Bearing Plants. 2023;26(5):1220–1235. DOI: 10.1080/0972060X.2023.2273264 [DOI]
  26. 26 Karuniawan A, Maulana H, Ustari D, Dewayani S, Solihin E, Solihin MA. Yield stability analysis of orange - Fleshed sweet potato in Indonesia using AMMI and GGE biplot. Heliyon. 2021;7(4):1–10. DOI: 10.1016/j.heliyon.2021.e06881 [DOI]
  27. 27 Zhang PP, Song H, Ke XW, Jin XJ, Yin LH, Liu Y. 2016. GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes. J Integr Agric 2016;15(6):1218–1227. DOI: 10.1016/S2095-3119(15)61157-1 [DOI]
  28. 28 Ruswandi D, Syafii M, Wicaksana N, Maulana H, Ariyanti M, Indriani NP et al., Evaluation of high yielding maize hybrids based on combined stability analysis, sustainability index, and GGE biplot. Biomed Res Int. 2022;1–12. DOI: 10.1155/2022/3963850 [DOI]
  29. 29 Vaezi B, Pour-Aboughadareh A, Mehraban A, Hossein-Pour T, Mohammadi R, Armion M. The use of parametric and non-parametric measures for selecting stable and adapted barley lines. Arch Agron Soil Sci. 2018;64(5):597–611.DOI: 10.1080/03650340.2017.1369529 [DOI]
  30. 30 Vaezi B, Pour-Aboughadareh A, Mohammadi R, Mehraban A, Hossein-Pour T, Koohkan E. Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes. Euphytica 2019;215(63):1–18. [DOI]
  31. 31 DOI: 10.1007/s10681-019-2386-5 [DOI]

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